Spaces:
Configuration error
Configuration error
File size: 5,172 Bytes
7e93a0e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import argparse
import cv2
import numpy as np
try:
from imwatermark import WatermarkDecoder
except ImportError as e:
try:
# Assume some of the other dependencies such as torch are not fulfilled
# import file without loading unnecessary libraries.
import importlib.util
import sys
spec = importlib.util.find_spec("imwatermark.maxDct")
assert spec is not None
maxDct = importlib.util.module_from_spec(spec)
sys.modules["maxDct"] = maxDct
spec.loader.exec_module(maxDct)
class WatermarkDecoder(object):
"""A minimal version of
https://github.com/ShieldMnt/invisible-watermark/blob/main/imwatermark/watermark.py
to only reconstruct bits using dwtDct"""
def __init__(self, wm_type="bytes", length=0):
assert wm_type == "bits", "Only bits defined in minimal import"
self._wmType = wm_type
self._wmLen = length
def reconstruct(self, bits):
if len(bits) != self._wmLen:
raise RuntimeError("bits are not matched with watermark length")
return bits
def decode(self, cv2Image, method="dwtDct", **configs):
(r, c, channels) = cv2Image.shape
if r * c < 256 * 256:
raise RuntimeError("image too small, should be larger than 256x256")
bits = []
assert method == "dwtDct"
embed = maxDct.EmbedMaxDct(watermarks=[], wmLen=self._wmLen, **configs)
bits = embed.decode(cv2Image)
return self.reconstruct(bits)
except:
raise e
# A fixed 48-bit message that was choosen at random
# WATERMARK_MESSAGE = 0xB3EC907BB19E
WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
MATCH_VALUES = [
[27, "No watermark detected"],
[33, "Partial watermark match. Cannot determine with certainty."],
[
35,
(
"Likely watermarked. In our test 0.02% of real images were "
'falsely detected as "Likely watermarked"'
),
],
[
49,
(
"Very likely watermarked. In our test no real images were "
'falsely detected as "Very likely watermarked"'
),
],
]
class GetWatermarkMatch:
def __init__(self, watermark):
self.watermark = watermark
self.num_bits = len(self.watermark)
self.decoder = WatermarkDecoder("bits", self.num_bits)
def __call__(self, x: np.ndarray) -> np.ndarray:
"""
Detects the number of matching bits the predefined watermark with one
or multiple images. Images should be in cv2 format, e.g. h x w x c BGR.
Args:
x: ([B], h w, c) in range [0, 255]
Returns:
number of matched bits ([B],)
"""
squeeze = len(x.shape) == 3
if squeeze:
x = x[None, ...]
bs = x.shape[0]
detected = np.empty((bs, self.num_bits), dtype=bool)
for k in range(bs):
detected[k] = self.decoder.decode(x[k], "dwtDct")
result = np.sum(detected == self.watermark, axis=-1)
if squeeze:
return result[0]
else:
return result
get_watermark_match = GetWatermarkMatch(WATERMARK_BITS)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"filename",
nargs="+",
type=str,
help="Image files to check for watermarks",
)
opts = parser.parse_args()
print(
"""
This script tries to detect watermarked images. Please be aware of
the following:
- As the watermark is supposed to be invisible, there is the risk that
watermarked images may not be detected.
- To maximize the chance of detection make sure that the image has the same
dimensions as when the watermark was applied (most likely 1024x1024
or 512x512).
- Specific image manipulation may drastically decrease the chance that
watermarks can be detected.
- There is also the chance that an image has the characteristics of the
watermark by chance.
- The watermark script is public, anybody may watermark any images, and
could therefore claim it to be generated.
- All numbers below are based on a test using 10,000 images without any
modifications after applying the watermark.
"""
)
for fn in opts.filename:
image = cv2.imread(fn)
if image is None:
print(f"Couldn't read {fn}. Skipping")
continue
num_bits = get_watermark_match(image)
k = 0
while num_bits > MATCH_VALUES[k][0]:
k += 1
print(
f"{fn}: {MATCH_VALUES[k][1]}",
f"Bits that matched the watermark {num_bits} from {len(WATERMARK_BITS)}\n",
sep="\n\t",
)
|